Month: July 2018

UTC-IASE Faculty Spotlight: Dr. Shalabh Gupta


This week’s faculty spotlight is on Dr. Shalabh Gupta, who is an associate professor in the Electrical and Computer Engineering Department at UConn. Dr. Gupta received a Bachelors of Technology in Mechanical Engineering from the Indian Institute of Technology-Roorkee. He then joined the  Pennsylvania State University where he received a Masters of Science in Mechanical Engineering, a Masters of Science in Electrical Engineering, and a PhD in Mechanical Engineering with focus on Systems Engineering. Before he joined UConn in 2011, he was a Research Associate in the Department of Mechanical and Nuclear Engineering at Pennsylvania State University from 2008-2011 and a Post-Doctoral Research Scholar for the department from 2006-2008. Currently, he is leading the Laboratory of Intelligent Networks and Knowledge-Perception Systems (LINKS) at UConn. Dr. Gupta holds a joint appointment with Management and Engineering for Manufacturing program.

In his professional career, Dr. Gupta has written over 100 research articles including book chapters, journal papers, patents, and conference papers. He is the Specialty Chief Editor of the journal “Frontiers in Robotics and AI” (Specialty: Sensor Fusion and Machine Perception)” since 2017. He has also served as an Associate Editor for “Structural health Monitoring- An International Journal” since 2010.  He served as the Program chair of the International Conference on Complex Systems Engineering organized by the UTC-Institute for Advanced Systems Engineering in 2015. He has supervised 15 senior design team, 3 of which received the top three positions in the ECE department in consecutive years.

 Dr. Gupta’s research is focused on the Science of Autonomy with emphasis on two key areas: Data Analytics and Networked-Intelligent systems. In essence, his research is centered around the essential characteristic of cyber-physical systems that links the domain of system dynamics with the domain of information & control. Some specific research areas include data selection, data reduction, and data interpretation; information fusion from heterogeneous sources for improved classification; 3C network autonomy via distributed classification, clustering and control in stochastic environment; path planning for autonomous vehicles- coverage path planning, time-optimal path planning, and safe path planning;  cooperative autonomy of unmanned vehicles; resilient control of complex systems in presence of failures; and fault diagnosis & prognosis in networked-control systems. Some examples of the application areas of his research include distributed sensor networks for Intelligence, Surveillance & Reconnaissance (ISR) operations, autonomous vehicles, smart buildings, smart grids, smart manufacturing, resilient infrastructures, and aerospace systems.

Recently, he published three articles which received good positive feedback. The first article “ε*: An Online Coverage Path Planning Algorithm” appeared in IEEE Transactions on Robotics (IEEE-TRO) in 2018.  Since publication, it has consistently achieved a rank in the top 15 of the most popular articles from all articles ever published in TRO.  The paper developed a novel algorithm for complete coverage path planning of unknown environment with theoretical guarantees. The applications include house cleaning robots, autonomous lawn mowers, underwater mine hunting, etc.

The second article “POSE: Prediction based Opportunistic Sensing for Energy-efficient Sensor Networks using Distributed Supervisors” appeared in IEEE Transactions on Cybernetics (IEEE-TOC) in 2018. The paper received significant media attention and was highlighted in “UConn Today” and “The Day”. The paper focuses on distributed probabilistic control of multi-modal sensor networks for energy-efficient target tracking. The applications include border surveillance, urban sensor networks, smart cities, etc.


The third article “Topological Characterization and Early Detection of Bifurcations and Chaos in Complex Systems using Persistent Homology” appeared in Chaos: An Interdisciplinary Journal of Nonlinear Science in 2017. The paper uses the concepts from algebraic topology for deeper insights into changes in topological  features in data as anomalies happen. The article was picked by the editor for Front Page Display on the Journal’s Website.


Dr. Gupta is currently working on the following research projects:

  1. Reconfigurable control of chiller plants via joint optimization of reliability and performance.  
  2. Real-time path planning of UUVs in complex environment.
  3. Data Selection and data reduction from big data for efficient analysis.
  4. Distributed control of sensor networks for 3C Network autonomy.


Dr. Gupta is also advising student research projects this summer. Khushboo Mittal, a student in the Electrical and Computer Engineering Department, is the recipient of a UTC fellowship. She is working on developing supervisory control concepts for complex systems with a focus on resilience to failures, reliability, and performance optimization. James Wilson, a student in the Electrical and Computer Engineering Department, is also the recipient of a UTC fellowship. He is working on developing novel data analytics tools for complex data.

UTC-IASE Faculty Spotlight: Amy Thompson


May the Cooling Season Begin…. Amy Thompson, Associate Professor-In-Residence at the UConn UTC Institute for Advanced Systems Engineering is studying the impact in the field of retrofitting rooftop HVAC systems with market-ready fault detection and diagnosis (FDD) equipment.



Through a multi-partner, $1.2 million, 3-year DOE EERE grant, the University of Connecticut, the University of New Haven, United Illuminating, Eversource, and United Technologies Research Center are studying the effectiveness and market barriers of fault detection and diagnosis (FDD) equipment for rooftop HVAC systems in the field in Connecticut. FDD technology has been studied in the laboratory, but no large-scale study of the technology in the field has been conducted in the U.S. Common HVAC faults that can be detected with FDD technologies include:


– Restricted indoor and outdoor airflow

– Incorrect refrigerant charge

– Refrigerant line blockage

– Malfunctioning expansion device

– Compressor valve leakage

– Non-condensable gases

– Short cycling

– Economizer Faults


Applications are now open to any Connecticut commercial or industrial organization that would like to become a study participant and study site. Benefits for sites include no-charge FDD retrofits for 1-2 rooftop units (RTUs), training on how to interpret FDD alarms and results, and a full report and analysis of the ability of the technology to lower energy usage and costs. Applications are open until June 30, 2018 here: or contact Amy Thompson at the UTC Institute for Advanced Systems Engineering at with questions.